Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres-Reference-Cited by-同舟云学术

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Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres

Published:2017-09 Issue: Volume:168 Page:15-27
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Chu Maoxiang,Zhao Jie,Liu Xiaoping,Gong Rongfen

Funder

State Key Laboratory of Robotics and System (HIT)

Liaoning Province PhD Start-up Fund

Liaoning Province Ministry of Education Scientific Study Project

University of Science and Technology Liaoning Foundation

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference41 articles.

1. Classification of steel samples by laser-induced breakdown spectroscopy and random forest;Zhang;Chemom. Intel. Labor. Syst.,2016

2. H. Wang, P. Shi, H. Li, Q. Zhou, Adaptive neural tracking control for a class of nonlinear systems with dynamic uncertainties, IEEE Trans. Cybern., http://dx.doi.org/10.1109/TCYB.2016.2607166.

3. H. Wang, P.X. Liu, P. Shi, Observer-based fuzzy adaptive output-feedback control of stochastic nonlinear multiple time-delay systems, IEEE Trans. Cybern., http://dx.doi.org/10.1109/TCYB.2017.2655501.

4. Real-time vision-based defect inspection for high-speed steel products;Yun;Opt. Eng.,2008

5. X. Duan, F. Duan, F. Han, Study on surface defect vision detection system for steel plate based on virtual instrument technology, in: International Conference on Control, Automation and Systems Engineering, 2011, pp.1–4.

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